Overview

Brought to you by YData

Dataset statistics

Number of variables38
Number of observations228707
Missing cells715195
Missing cells (%)8.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory66.3 MiB
Average record size in memory304.0 B

Variable types

Categorical16
Numeric11
Text8
DateTime1
Boolean2

Alerts

anio has constant value "2024" Constant
iny_co2 has constant value "0.0" Constant
vida_util has constant value "0.0" Constant
rectificado has constant value "False" Constant
habilitado has constant value "True" Constant
clasificacion is highly overall correlated with subclasificacionHigh correlation
cuenca is highly overall correlated with empresa and 2 other fieldsHigh correlation
empresa is highly overall correlated with cuenca and 4 other fieldsHigh correlation
fecha_data is highly overall correlated with mesHigh correlation
idempresa is highly overall correlated with cuenca and 4 other fieldsHigh correlation
idusuario is highly overall correlated with empresa and 1 other fieldsHigh correlation
iny_agua is highly overall correlated with sub_tipo_recursoHigh correlation
iny_otro is highly overall correlated with sub_tipo_recursoHigh correlation
mes is highly overall correlated with fecha_dataHigh correlation
provincia is highly overall correlated with cuenca and 2 other fieldsHigh correlation
proyecto is highly overall correlated with sub_tipo_recursoHigh correlation
sub_tipo_recurso is highly overall correlated with empresa and 6 other fieldsHigh correlation
subclasificacion is highly overall correlated with clasificacionHigh correlation
tipo_de_recurso is highly overall correlated with sub_tipo_recursoHigh correlation
tipopozo is highly overall correlated with sub_tipo_recursoHigh correlation
tipoestado is highly imbalanced (96.5%) Imbalance
tipopozo is highly imbalanced (73.6%) Imbalance
tipo_de_recurso is highly imbalanced (73.1%) Imbalance
proyecto is highly imbalanced (79.6%) Imbalance
clasificacion is highly imbalanced (87.1%) Imbalance
subclasificacion is highly imbalanced (81.5%) Imbalance
vida_util has 228704 (> 99.9%) missing values Missing
observaciones has 218151 (95.4%) missing values Missing
clasificacion has 33651 (14.7%) missing values Missing
subclasificacion has 33651 (14.7%) missing values Missing
sub_tipo_recurso has 200650 (87.7%) missing values Missing
prod_gas is highly skewed (γ1 = 30.07153205) Skewed
iny_agua is highly skewed (γ1 = 205.7757769) Skewed
iny_gas is highly skewed (γ1 = 92.31600415) Skewed
iny_otro is highly skewed (γ1 = 148.5224838) Skewed
profundidad is highly skewed (γ1 = 110.5183505) Skewed
prod_pet has 10005 (4.4%) zeros Zeros
prod_gas has 56968 (24.9%) zeros Zeros
prod_agua has 11111 (4.9%) zeros Zeros
iny_agua has 228627 (> 99.9%) zeros Zeros
iny_gas has 228649 (> 99.9%) zeros Zeros
iny_otro has 228687 (> 99.9%) zeros Zeros
profundidad has 14875 (6.5%) zeros Zeros

Reproduction

Analysis started2024-11-08 21:38:23.416521
Analysis finished2024-11-08 21:39:38.119132
Duration1 minute and 14.7 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

idempresa
Categorical

High correlation 

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
YPF
106670 
PAL
43315 
CG1
12619 
PLU
 
9253
PCR
 
8597
Other values (42)
48253 

Length

Max length4
Median length3
Mean length3.0300122
Min length3

Characters and Unicode

Total characters692985
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYPF
2nd rowYPF
3rd rowYPF
4th rowYPF
5th rowYPF

Common Values

ValueCountFrequency (%)
YPF 106670
46.6%
PAL 43315
18.9%
CG1 12619
 
5.5%
PLU 9253
 
4.0%
PCR 8597
 
3.8%
ACO 6897
 
3.0%
APS 6373
 
2.8%
CAP 5583
 
2.4%
TPT 5124
 
2.2%
OILS 3808
 
1.7%
Other values (37) 20468
 
8.9%

Length

2024-11-08T21:39:38.373757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ypf 106670
46.6%
pal 43315
18.9%
cg1 12619
 
5.5%
plu 9253
 
4.0%
pcr 8597
 
3.8%
aco 6897
 
3.0%
aps 6373
 
2.8%
cap 5583
 
2.4%
tpt 5124
 
2.2%
oils 3808
 
1.7%
Other values (37) 20468
 
8.9%

Most occurring characters

ValueCountFrequency (%)
P 191954
27.7%
F 106826
15.4%
Y 106670
15.4%
A 68870
 
9.9%
L 56964
 
8.2%
C 42996
 
6.2%
S 16357
 
2.4%
T 16269
 
2.3%
G 15891
 
2.3%
U 12791
 
1.8%
Other values (14) 57397
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 692985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 191954
27.7%
F 106826
15.4%
Y 106670
15.4%
A 68870
 
9.9%
L 56964
 
8.2%
C 42996
 
6.2%
S 16357
 
2.4%
T 16269
 
2.3%
G 15891
 
2.3%
U 12791
 
1.8%
Other values (14) 57397
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 692985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 191954
27.7%
F 106826
15.4%
Y 106670
15.4%
A 68870
 
9.9%
L 56964
 
8.2%
C 42996
 
6.2%
S 16357
 
2.4%
T 16269
 
2.3%
G 15891
 
2.3%
U 12791
 
1.8%
Other values (14) 57397
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 692985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 191954
27.7%
F 106826
15.4%
Y 106670
15.4%
A 68870
 
9.9%
L 56964
 
8.2%
C 42996
 
6.2%
S 16357
 
2.4%
T 16269
 
2.3%
G 15891
 
2.3%
U 12791
 
1.8%
Other values (14) 57397
 
8.3%

anio
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2024
228707 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters914828
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 228707
100.0%

Length

2024-11-08T21:39:38.691783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T21:39:38.933022image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2024 228707
100.0%

Most occurring characters

ValueCountFrequency (%)
2 457414
50.0%
0 228707
25.0%
4 228707
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 914828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 457414
50.0%
0 228707
25.0%
4 228707
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 914828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 457414
50.0%
0 228707
25.0%
4 228707
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 914828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 457414
50.0%
0 228707
25.0%
4 228707
25.0%

mes
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9847141
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-08T21:39:39.137238image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.582446
Coefficient of variation (CV)0.51807304
Kurtosis-1.2284927
Mean4.9847141
Median Absolute Deviation (MAD)2
Skewness0.010032229
Sum1140039
Variance6.6690273
MonotonicityNot monotonic
2024-11-08T21:39:39.424430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 25618
11.2%
1 25611
11.2%
2 25559
11.2%
4 25554
11.2%
3 25543
11.2%
6 25346
11.1%
9 25341
11.1%
8 25285
11.1%
7 24850
10.9%
ValueCountFrequency (%)
1 25611
11.2%
2 25559
11.2%
3 25543
11.2%
4 25554
11.2%
5 25618
11.2%
6 25346
11.1%
7 24850
10.9%
8 25285
11.1%
9 25341
11.1%
ValueCountFrequency (%)
9 25341
11.1%
8 25285
11.1%
7 24850
10.9%
6 25346
11.1%
5 25618
11.2%
4 25554
11.2%
3 25543
11.2%
2 25559
11.2%
1 25611
11.2%

idpozo
Real number (ℝ)

Distinct27613
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117252.95
Minimum212
Maximum165693
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-08T21:39:39.744114image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum212
5-th percentile35642
Q197898
median129754
Q3155503
95-th percentile163794
Maximum165693
Range165481
Interquartile range (IQR)57605

Descriptive statistics

Standard deviation43074.146
Coefficient of variation (CV)0.36736089
Kurtosis-0.11958806
Mean117252.95
Median Absolute Deviation (MAD)28401
Skewness-0.90514244
Sum2.6816569 × 1010
Variance1.8553821 × 109
MonotonicityNot monotonic
2024-11-08T21:39:40.109501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162223 9
 
< 0.1%
153504 9
 
< 0.1%
47394 9
 
< 0.1%
35501 9
 
< 0.1%
48739 9
 
< 0.1%
52142 9
 
< 0.1%
164693 9
 
< 0.1%
156889 9
 
< 0.1%
49303 9
 
< 0.1%
48499 9
 
< 0.1%
Other values (27603) 228617
> 99.9%
ValueCountFrequency (%)
212 9
< 0.1%
214 9
< 0.1%
215 9
< 0.1%
216 8
< 0.1%
217 9
< 0.1%
218 9
< 0.1%
219 9
< 0.1%
223 8
< 0.1%
225 9
< 0.1%
226 9
< 0.1%
ValueCountFrequency (%)
165693 1
< 0.1%
165690 1
< 0.1%
165689 1
< 0.1%
165688 1
< 0.1%
165687 1
< 0.1%
165686 1
< 0.1%
165685 1
< 0.1%
165684 1
< 0.1%
165683 1
< 0.1%
165682 1
< 0.1%

prod_pet
Real number (ℝ)

Zeros 

Distinct120807
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.25339
Minimum-0.01
Maximum18034.332
Zeros10005
Zeros (%)4.4%
Negative1
Negative (%)< 0.1%
Memory size1.7 MiB
2024-11-08T21:39:40.468250image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.01
5-th percentile0.1
Q114.54
median38.03
Q385.74
95-th percentile412.67393
Maximum18034.332
Range18034.342
Interquartile range (IQR)71.2

Descriptive statistics

Standard deviation470.74405
Coefficient of variation (CV)3.55941
Kurtosis143.49178
Mean132.25339
Median Absolute Deviation (MAD)29.5
Skewness10.014431
Sum30247276
Variance221599.96
MonotonicityNot monotonic
2024-11-08T21:39:40.822699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10005
 
4.4%
0.03 165
 
0.1%
0.04 161
 
0.1%
0.05 126
 
0.1%
0.06 120
 
0.1%
0.02 111
 
< 0.1%
0.29 95
 
< 0.1%
2.2 82
 
< 0.1%
0.7 82
 
< 0.1%
0.14 79
 
< 0.1%
Other values (120797) 217681
95.2%
ValueCountFrequency (%)
-0.01 1
 
< 0.1%
0 10005
4.4%
1.30381 × 10-61
 
< 0.1%
9 × 10-61
 
< 0.1%
1.2 × 10-51
 
< 0.1%
8.8 × 10-51
 
< 0.1%
9 × 10-51
 
< 0.1%
9.3 × 10-52
 
< 0.1%
9.6 × 10-52
 
< 0.1%
0.000149 1
 
< 0.1%
ValueCountFrequency (%)
18034.332 1
< 0.1%
16436.799 1
< 0.1%
14645.788 1
< 0.1%
14528.627 1
< 0.1%
14388.425 1
< 0.1%
14135.351 1
< 0.1%
13986.883 1
< 0.1%
13349.109 1
< 0.1%
13162.499 1
< 0.1%
12247.552 1
< 0.1%

prod_gas
Real number (ℝ)

Skewed  Zeros 

Distinct95050
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.22577
Minimum-0.01
Maximum85016.22
Zeros56968
Zeros (%)24.9%
Negative1
Negative (%)< 0.1%
Memory size1.7 MiB
2024-11-08T21:39:41.180073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.01
5-th percentile0
Q10.0002845
median3.147035
Q321.050395
95-th percentile510.23309
Maximum85016.22
Range85016.23
Interquartile range (IQR)21.050111

Descriptive statistics

Standard deviation1322.04
Coefficient of variation (CV)7.7663914
Kurtosis1368.8368
Mean170.22577
Median Absolute Deviation (MAD)3.147035
Skewness30.071532
Sum38931826
Variance1747789.7
MonotonicityNot monotonic
2024-11-08T21:39:41.552192image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56968
 
24.9%
0.06 537
 
0.2%
0.37 422
 
0.2%
0.03 330
 
0.1%
0.3 329
 
0.1%
0.38 318
 
0.1%
0.31 305
 
0.1%
0.32 261
 
0.1%
0.01 243
 
0.1%
0.35 230
 
0.1%
Other values (95040) 168764
73.8%
ValueCountFrequency (%)
-0.01 1
 
< 0.1%
0 56968
24.9%
1 × 10-65
 
< 0.1%
2 × 10-61
 
< 0.1%
3 × 10-64
 
< 0.1%
4 × 10-62
 
< 0.1%
5 × 10-62
 
< 0.1%
6 × 10-64
 
< 0.1%
7 × 10-61
 
< 0.1%
9 × 10-61
 
< 0.1%
ValueCountFrequency (%)
85016.22 1
< 0.1%
78533.96 1
< 0.1%
73944.48 1
< 0.1%
73885.99 1
< 0.1%
73756.24 1
< 0.1%
73581.76 1
< 0.1%
72918.75 1
< 0.1%
72784.8 1
< 0.1%
72715.41 1
< 0.1%
72624.48 1
< 0.1%

prod_agua
Real number (ℝ)

Zeros 

Distinct169553
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1106.9498
Minimum-0.51
Maximum31903.92
Zeros11111
Zeros (%)4.9%
Negative1
Negative (%)< 0.1%
Memory size1.7 MiB
2024-11-08T21:39:41.873954image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.51
5-th percentile0.01
Q134.07042
median289.05
Q31374.572
95-th percentile4850.328
Maximum31903.92
Range31904.43
Interquartile range (IQR)1340.5016

Descriptive statistics

Standard deviation1887.1104
Coefficient of variation (CV)1.7047841
Kurtosis16.1507
Mean1106.9498
Median Absolute Deviation (MAD)286.37
Skewness3.2959259
Sum2.5316717 × 108
Variance3561185.7
MonotonicityNot monotonic
2024-11-08T21:39:42.446879image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11111
 
4.9%
0.01 764
 
0.3%
0.02 225
 
0.1%
0.05 163
 
0.1%
0.04 146
 
0.1%
0.06 142
 
0.1%
0.28 129
 
0.1%
0.13 127
 
0.1%
0.03 126
 
0.1%
0.24 120
 
0.1%
Other values (169543) 215654
94.3%
ValueCountFrequency (%)
-0.51 1
 
< 0.1%
0 11111
4.9%
1.9 × 10-51
 
< 0.1%
0.000288 1
 
< 0.1%
0.000362 1
 
< 0.1%
0.00043 1
 
< 0.1%
0.000522 1
 
< 0.1%
0.000533 1
 
< 0.1%
0.000542 1
 
< 0.1%
0.000565 1
 
< 0.1%
ValueCountFrequency (%)
31903.92 1
< 0.1%
25119.22 1
< 0.1%
24615.83 1
< 0.1%
24598.85 1
< 0.1%
24339.52 1
< 0.1%
23733.15 1
< 0.1%
23586.33 1
< 0.1%
23342.15 1
< 0.1%
23234.04 1
< 0.1%
22704.89 1
< 0.1%

iny_agua
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.078986135
Minimum0
Maximum3748
Zeros228627
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-08T21:39:43.052500image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3748
Range3748
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.636016
Coefficient of variation (CV)172.63809
Kurtosis46029.542
Mean0.078986135
Median Absolute Deviation (MAD)0
Skewness205.77578
Sum18064.682
Variance185.94093
MonotonicityNot monotonic
2024-11-08T21:39:43.674233image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 228627
> 99.9%
1.88 2
 
< 0.1%
2.73 2
 
< 0.1%
7.58 2
 
< 0.1%
1.98 2
 
< 0.1%
9.81 2
 
< 0.1%
15.93 2
 
< 0.1%
5.93 1
 
< 0.1%
3.2 1
 
< 0.1%
7.42 1
 
< 0.1%
Other values (65) 65
 
< 0.1%
ValueCountFrequency (%)
0 228627
> 99.9%
6.9 × 10-51
 
< 0.1%
0.000148 1
 
< 0.1%
0.000276 1
 
< 0.1%
0.000414 1
 
< 0.1%
0.000443 1
 
< 0.1%
0.00065 1
 
< 0.1%
0.98 1
 
< 0.1%
1.36 1
 
< 0.1%
1.67 1
 
< 0.1%
ValueCountFrequency (%)
3748 1
< 0.1%
3054.15 1
< 0.1%
2350 1
< 0.1%
2240 1
< 0.1%
1838 1
< 0.1%
1796 1
< 0.1%
1200 1
< 0.1%
527 1
< 0.1%
400 1
< 0.1%
329.69 1
< 0.1%

iny_gas
Real number (ℝ)

Skewed  Zeros 

Distinct57
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.057322032
Minimum0
Maximum593.03
Zeros228649
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-08T21:39:44.250791image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum593.03
Range593.03
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.5706784
Coefficient of variation (CV)79.736853
Kurtosis9284.6007
Mean0.057322032
Median Absolute Deviation (MAD)0
Skewness92.316004
Sum13109.95
Variance20.891101
MonotonicityNot monotonic
2024-11-08T21:39:44.771966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 228649
> 99.9%
0.03 2
 
< 0.1%
0.01 2
 
< 0.1%
299.76 1
 
< 0.1%
181.13 1
 
< 0.1%
292.65 1
 
< 0.1%
153.64 1
 
< 0.1%
499.58 1
 
< 0.1%
279.89 1
 
< 0.1%
233.82 1
 
< 0.1%
Other values (47) 47
 
< 0.1%
ValueCountFrequency (%)
0 228649
> 99.9%
0.01 2
 
< 0.1%
0.03 2
 
< 0.1%
0.17 1
 
< 0.1%
0.33 1
 
< 0.1%
0.75 1
 
< 0.1%
2.8 1
 
< 0.1%
12.1 1
 
< 0.1%
18.71 1
 
< 0.1%
28.8 1
 
< 0.1%
ValueCountFrequency (%)
593.03 1
< 0.1%
569.48 1
< 0.1%
555.27 1
< 0.1%
540.18 1
< 0.1%
539.58 1
< 0.1%
499.58 1
< 0.1%
475.72 1
< 0.1%
450.81 1
< 0.1%
434.47 1
< 0.1%
411.67 1
< 0.1%

iny_co2
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
228707 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters686121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 228707
100.0%

Length

2024-11-08T21:39:45.425772image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T21:39:45.894283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 228707
100.0%

Most occurring characters

ValueCountFrequency (%)
0 457414
66.7%
. 228707
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 686121
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 457414
66.7%
. 228707
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 686121
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 457414
66.7%
. 228707
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 686121
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 457414
66.7%
. 228707
33.3%

iny_otro
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0013365573
Minimum0
Maximum31
Zeros228687
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-08T21:39:46.408906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum31
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.16516026
Coefficient of variation (CV)123.57141
Kurtosis24313.345
Mean0.0013365573
Median Absolute Deviation (MAD)0
Skewness148.52248
Sum305.68
Variance0.027277912
MonotonicityNot monotonic
2024-11-08T21:39:46.677158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 228687
> 99.9%
7 5
 
< 0.1%
13.67 3
 
< 0.1%
28.5 2
 
< 0.1%
14.25 2
 
< 0.1%
31 1
 
< 0.1%
30 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
15.67 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
ValueCountFrequency (%)
0 228687
> 99.9%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 5
 
< 0.1%
13 1
 
< 0.1%
13.67 3
 
< 0.1%
14 1
 
< 0.1%
14.25 2
 
< 0.1%
15.67 1
 
< 0.1%
28.5 2
 
< 0.1%
ValueCountFrequency (%)
31 1
 
< 0.1%
30 1
 
< 0.1%
29.5 1
 
< 0.1%
28.5 2
 
< 0.1%
15.67 1
 
< 0.1%
14.25 2
 
< 0.1%
14 1
 
< 0.1%
13.67 3
< 0.1%
13 1
 
< 0.1%
7 5
< 0.1%

tef
Real number (ℝ)

Distinct13938
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.904916
Minimum-0.01
Maximum31
Zeros84
Zeros (%)< 0.1%
Negative1
Negative (%)< 0.1%
Memory size1.7 MiB
2024-11-08T21:39:47.010511image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.01
5-th percentile14.52
Q128.666667
median30
Q330.91667
95-th percentile31
Maximum31
Range31.01
Interquartile range (IQR)2.2500033

Descriptive statistics

Standard deviation5.6989014
Coefficient of variation (CV)0.20422571
Kurtosis8.2400312
Mean27.904916
Median Absolute Deviation (MAD)1
Skewness-2.8704571
Sum6382049.7
Variance32.477477
MonotonicityNot monotonic
2024-11-08T21:39:47.349247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 43379
 
19.0%
30 25807
 
11.3%
29 9673
 
4.2%
30.96 1923
 
0.8%
30.92 1483
 
0.6%
29.96 1319
 
0.6%
30.75 1122
 
0.5%
30.98 1110
 
0.5%
30.91667 1016
 
0.4%
30.88 1007
 
0.4%
Other values (13928) 140868
61.6%
ValueCountFrequency (%)
-0.01 1
 
< 0.1%
0 84
< 0.1%
0.000694 5
 
< 0.1%
0.000833333 1
 
< 0.1%
0.001 6
 
< 0.1%
0.001389 4
 
< 0.1%
0.002778 1
 
< 0.1%
0.003472 7
 
< 0.1%
0.006944 5
 
< 0.1%
0.01 3
 
< 0.1%
ValueCountFrequency (%)
31 43379
19.0%
30.99931 22
 
< 0.1%
30.999 1
 
< 0.1%
30.99861 5
 
< 0.1%
30.99792 3
 
< 0.1%
30.99722 1
 
< 0.1%
30.99653 30
 
< 0.1%
30.99583333 5
 
< 0.1%
30.995 1
 
< 0.1%
30.99444 1
 
< 0.1%

vida_util
Categorical

Constant  Missing 

Distinct1
Distinct (%)33.3%
Missing228704
Missing (%)> 99.9%
Memory size1.7 MiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0

Common Values

ValueCountFrequency (%)
0.0 3
 
< 0.1%
(Missing) 228704
> 99.9%

Length

2024-11-08T21:39:47.666417image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T21:39:47.925116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3
100.0%

Most occurring characters

ValueCountFrequency (%)
0 6
66.7%
. 3
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6
66.7%
. 3
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6
66.7%
. 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6
66.7%
. 3
33.3%

tipoextraccion
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Bombeo Mecánico
115762 
Surgencia Natural
34961 
Electrosumergible
32360 
Cavidad Progresiva
31021 
Plunger Lift
 
6574
Other values (6)
 
8029

Length

Max length25
Median length15
Mean length15.918052
Min length8

Characters and Unicode

Total characters3640570
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBombeo Mecánico
2nd rowBombeo Mecánico
3rd rowBombeo Mecánico
4th rowBombeo Mecánico
5th rowCavidad Progresiva

Common Values

ValueCountFrequency (%)
Bombeo Mecánico 115762
50.6%
Surgencia Natural 34961
 
15.3%
Electrosumergible 32360
 
14.1%
Cavidad Progresiva 31021
 
13.6%
Plunger Lift 6574
 
2.9%
Pistoneo (Swabbing) 4526
 
2.0%
Gas Lift 2929
 
1.3%
Sin Sistema de Extracción 330
 
0.1%
Otros Tipos de Extracción 163
 
0.1%
Jet Pump 79
 
< 0.1%

Length

2024-11-08T21:39:48.194277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bombeo 115764
27.2%
mecánico 115762
27.2%
surgencia 34961
 
8.2%
natural 34961
 
8.2%
electrosumergible 32360
 
7.6%
cavidad 31021
 
7.3%
progresiva 31021
 
7.3%
lift 9503
 
2.2%
plunger 6574
 
1.5%
swabbing 4526
 
1.1%
Other values (11) 9587
 
2.3%

Most occurring characters

ValueCountFrequency (%)
o 420051
 
11.5%
e 406590
 
11.2%
c 299833
 
8.2%
i 265000
 
7.3%
a 206224
 
5.7%
r 203916
 
5.6%
197333
 
5.4%
n 167172
 
4.6%
b 157176
 
4.3%
m 148533
 
4.1%
Other values (28) 1168742
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3640570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 420051
 
11.5%
e 406590
 
11.2%
c 299833
 
8.2%
i 265000
 
7.3%
a 206224
 
5.7%
r 203916
 
5.6%
197333
 
5.4%
n 167172
 
4.6%
b 157176
 
4.3%
m 148533
 
4.1%
Other values (28) 1168742
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3640570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 420051
 
11.5%
e 406590
 
11.2%
c 299833
 
8.2%
i 265000
 
7.3%
a 206224
 
5.7%
r 203916
 
5.6%
197333
 
5.4%
n 167172
 
4.6%
b 157176
 
4.3%
m 148533
 
4.1%
Other values (28) 1168742
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3640570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 420051
 
11.5%
e 406590
 
11.2%
c 299833
 
8.2%
i 265000
 
7.3%
a 206224
 
5.7%
r 203916
 
5.6%
197333
 
5.4%
n 167172
 
4.6%
b 157176
 
4.3%
m 148533
 
4.1%
Other values (28) 1168742
32.1%

tipoestado
Categorical

Imbalance 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Extracción Efectiva
224969 
Parado Transitoriamente
 
3555
En Estudio
 
73
En Inyección Efectiva
 
25
Otras Situación Activo
 
24
Other values (7)
 
61

Length

Max length34
Median length19
Mean length19.062246
Min length10

Characters and Unicode

Total characters4359669
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowExtracción Efectiva
2nd rowExtracción Efectiva
3rd rowExtracción Efectiva
4th rowExtracción Efectiva
5th rowExtracción Efectiva

Common Values

ValueCountFrequency (%)
Extracción Efectiva 224969
98.4%
Parado Transitoriamente 3555
 
1.6%
En Estudio 73
 
< 0.1%
En Inyección Efectiva 25
 
< 0.1%
Otras Situación Activo 24
 
< 0.1%
Parado Alta Relación Agua/Petróleo 16
 
< 0.1%
En Espera de Reparación 16
 
< 0.1%
En Reserva para Recup. Sec./Asist. 12
 
< 0.1%
Mantenimiento de Presión 8
 
< 0.1%
Otras Situación Inactivo 7
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2024-11-08T21:39:49.119200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
efectiva 224994
49.2%
extracción 224969
49.2%
parado 3571
 
0.8%
transitoriamente 3555
 
0.8%
en 128
 
< 0.1%
estudio 73
 
< 0.1%
otras 31
 
< 0.1%
situación 31
 
< 0.1%
inyección 25
 
< 0.1%
de 25
 
< 0.1%
Other values (14) 178
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 675101
15.5%
a 464428
10.7%
i 457333
10.5%
t 457299
10.5%
E 450180
10.3%
r 235763
 
5.4%
n 232360
 
5.3%
e 232309
 
5.3%
228873
 
5.2%
ó 225082
 
5.2%
Other values (23) 700941
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4359669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 675101
15.5%
a 464428
10.7%
i 457333
10.5%
t 457299
10.5%
E 450180
10.3%
r 235763
 
5.4%
n 232360
 
5.3%
e 232309
 
5.3%
228873
 
5.2%
ó 225082
 
5.2%
Other values (23) 700941
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4359669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 675101
15.5%
a 464428
10.7%
i 457333
10.5%
t 457299
10.5%
E 450180
10.3%
r 235763
 
5.4%
n 232360
 
5.3%
e 232309
 
5.3%
228873
 
5.2%
ó 225082
 
5.2%
Other values (23) 700941
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4359669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 675101
15.5%
a 464428
10.7%
i 457333
10.5%
t 457299
10.5%
E 450180
10.3%
r 235763
 
5.4%
n 232360
 
5.3%
e 232309
 
5.3%
228873
 
5.2%
ó 225082
 
5.2%
Other values (23) 700941
16.1%

tipopozo
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Petrolífero
194825 
Gasífero
33698 
Otro tipo
 
143
Acuífero
 
26
Inyección de Agua
 
15

Length

Max length17
Median length11
Mean length10.556778
Min length8

Characters and Unicode

Total characters2414409
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPetrolífero
2nd rowPetrolífero
3rd rowPetrolífero
4th rowPetrolífero
5th rowPetrolífero

Common Values

ValueCountFrequency (%)
Petrolífero 194825
85.2%
Gasífero 33698
 
14.7%
Otro tipo 143
 
0.1%
Acuífero 26
 
< 0.1%
Inyección de Agua 15
 
< 0.1%

Length

2024-11-08T21:39:49.388515image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T21:39:49.639979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
petrolífero 194825
85.1%
gasífero 33698
 
14.7%
otro 143
 
0.1%
tipo 143
 
0.1%
acuífero 26
 
< 0.1%
inyección 15
 
< 0.1%
de 15
 
< 0.1%
agua 15
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 423660
17.5%
r 423517
17.5%
e 423404
17.5%
í 228549
9.5%
f 228549
9.5%
t 195111
8.1%
P 194825
8.1%
l 194825
8.1%
a 33713
 
1.4%
G 33698
 
1.4%
Other values (14) 34558
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2414409
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 423660
17.5%
r 423517
17.5%
e 423404
17.5%
í 228549
9.5%
f 228549
9.5%
t 195111
8.1%
P 194825
8.1%
l 194825
8.1%
a 33713
 
1.4%
G 33698
 
1.4%
Other values (14) 34558
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2414409
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 423660
17.5%
r 423517
17.5%
e 423404
17.5%
í 228549
9.5%
f 228549
9.5%
t 195111
8.1%
P 194825
8.1%
l 194825
8.1%
a 33713
 
1.4%
G 33698
 
1.4%
Other values (14) 34558
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2414409
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 423660
17.5%
r 423517
17.5%
e 423404
17.5%
í 228549
9.5%
f 228549
9.5%
t 195111
8.1%
P 194825
8.1%
l 194825
8.1%
a 33713
 
1.4%
G 33698
 
1.4%
Other values (14) 34558
 
1.4%

observaciones
Text

Missing 

Distinct100
Distinct (%)0.9%
Missing218151
Missing (%)95.4%
Memory size1.7 MiB
2024-11-08T21:39:49.962893image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length80
Median length3
Mean length9.5971959
Min length1

Characters and Unicode

Total characters101308
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.2%

Sample

1st rowNo son punzados nuevos, producci�³n previamente declarada en otra formaci�³n
2nd rowNo son punzados nuevos, producci�³n previamente declarada en otra formaci�³n
3rd rowNo son punzados nuevos, producci�³n previamente declarada en otra formaci�³n
4th rowNo son punzados nuevos, producci�³n previamente declarada en otra formaci�³n
5th rowNo son punzados nuevos, producci�³n previamente declarada en otra formaci�³n
ValueCountFrequency (%)
aco 6884
38.2%
en 1248
 
6.9%
extraccion 1032
 
5.7%
efectiva 1032
 
5.7%
de 942
 
5.2%
asistida 933
 
5.2%
productor 932
 
5.2%
recuperacion 932
 
5.2%
423
 
2.3%
so 375
 
2.1%
Other values (154) 3291
18.3%
2024-11-08T21:39:50.678138image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 8097
 
8.0%
7475
 
7.4%
O 7444
 
7.3%
C 7060
 
7.0%
c 6801
 
6.7%
i 5772
 
5.7%
a 5658
 
5.6%
o 5559
 
5.5%
e 5213
 
5.1%
r 4939
 
4.9%
Other values (63) 37290
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 101308
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 8097
 
8.0%
7475
 
7.4%
O 7444
 
7.3%
C 7060
 
7.0%
c 6801
 
6.7%
i 5772
 
5.7%
a 5658
 
5.6%
o 5559
 
5.5%
e 5213
 
5.1%
r 4939
 
4.9%
Other values (63) 37290
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 101308
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 8097
 
8.0%
7475
 
7.4%
O 7444
 
7.3%
C 7060
 
7.0%
c 6801
 
6.7%
i 5772
 
5.7%
a 5658
 
5.6%
o 5559
 
5.5%
e 5213
 
5.1%
r 4939
 
4.9%
Other values (63) 37290
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 101308
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 8097
 
8.0%
7475
 
7.4%
O 7444
 
7.3%
C 7060
 
7.0%
c 6801
 
6.7%
i 5772
 
5.7%
a 5658
 
5.6%
o 5559
 
5.5%
e 5213
 
5.1%
r 4939
 
4.9%
Other values (63) 37290
36.8%
Distinct1274
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Minimum2024-02-02 13:05:58.718803
Maximum2024-10-25 11:15:12.445520
2024-11-08T21:39:51.034920image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:51.365259image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

rectificado
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size223.5 KiB
False
228707 
ValueCountFrequency (%)
False 228707
100.0%
2024-11-08T21:39:51.634694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

habilitado
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size223.5 KiB
True
228707 
ValueCountFrequency (%)
True 228707
100.0%
2024-11-08T21:39:51.807604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

idusuario
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean381.71724
Minimum334
Maximum481
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-08T21:39:52.074692image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile334
Q1334
median345
Q3462
95-th percentile473
Maximum481
Range147
Interquartile range (IQR)128

Descriptive statistics

Standard deviation56.89634
Coefficient of variation (CV)0.14905363
Kurtosis-1.4009898
Mean381.71724
Median Absolute Deviation (MAD)11
Skewness0.6398081
Sum87301405
Variance3237.1935
MonotonicityNot monotonic
2024-11-08T21:39:52.421865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
334 106670
46.6%
462 43315
18.9%
473 12619
 
5.5%
381 9253
 
4.0%
345 8597
 
3.8%
459 6897
 
3.0%
383 6373
 
2.8%
353 5583
 
2.4%
364 5124
 
2.2%
420 3808
 
1.7%
Other values (37) 20468
 
8.9%
ValueCountFrequency (%)
334 106670
46.6%
345 8597
 
3.8%
346 73
 
< 0.1%
350 307
 
0.1%
353 5583
 
2.4%
355 89
 
< 0.1%
357 2791
 
1.2%
364 5124
 
2.2%
367 2782
 
1.2%
368 397
 
0.2%
ValueCountFrequency (%)
481 13
 
< 0.1%
480 220
 
0.1%
478 329
 
0.1%
477 44
 
< 0.1%
476 1366
 
0.6%
475 183
 
0.1%
474 31
 
< 0.1%
473 12619
5.5%
471 102
 
< 0.1%
470 290
 
0.1%

empresa
Categorical

High correlation 

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
YPF S.A.
106670 
PAN AMERICAN ENERGY SL
43315 
CGC ENERGIA SAU
12619 
PLUSPETROL S.A.
 
9253
PETROQUIMICA COMODORO RIVADAVIA S.A.
 
8597
Other values (42)
48253 

Length

Max length41
Median length39
Mean length15.609129
Min length8

Characters and Unicode

Total characters3569917
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYPF S.A.
2nd rowYPF S.A.
3rd rowYPF S.A.
4th rowYPF S.A.
5th rowYPF S.A.

Common Values

ValueCountFrequency (%)
YPF S.A. 106670
46.6%
PAN AMERICAN ENERGY SL 43315
18.9%
CGC ENERGIA SAU 12619
 
5.5%
PLUSPETROL S.A. 9253
 
4.0%
PETROQUIMICA COMODORO RIVADAVIA S.A. 8597
 
3.8%
Petrolera Aconcagua Energia S.A. 6897
 
3.0%
CAPEX S.A. 6373
 
2.8%
COMPAÑÍAS ASOCIADAS PETROLERAS S.A. 5583
 
2.4%
TECPETROL S.A. 5124
 
2.2%
OILSTONE ENERGIA S.A. 3808
 
1.7%
Other values (37) 20468
 
8.9%

Length

2024-11-08T21:39:52.802747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s.a 166621
26.4%
ypf 106670
16.9%
energy 45822
 
7.3%
pan 43315
 
6.9%
sl 43315
 
6.9%
american 43315
 
6.9%
energia 24781
 
3.9%
sau 14087
 
2.2%
cgc 12619
 
2.0%
pluspetrol 9305
 
1.5%
Other values (74) 120925
19.2%

Most occurring characters

ValueCountFrequency (%)
A 457549
12.8%
402593
11.3%
. 344992
 
9.7%
S 283993
 
8.0%
E 264166
 
7.4%
P 219568
 
6.2%
R 187787
 
5.3%
N 179165
 
5.0%
Y 152416
 
4.3%
I 122543
 
3.4%
Other values (35) 955145
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3569917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 457549
12.8%
402593
11.3%
. 344992
 
9.7%
S 283993
 
8.0%
E 264166
 
7.4%
P 219568
 
6.2%
R 187787
 
5.3%
N 179165
 
5.0%
Y 152416
 
4.3%
I 122543
 
3.4%
Other values (35) 955145
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3569917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 457549
12.8%
402593
11.3%
. 344992
 
9.7%
S 283993
 
8.0%
E 264166
 
7.4%
P 219568
 
6.2%
R 187787
 
5.3%
N 179165
 
5.0%
Y 152416
 
4.3%
I 122543
 
3.4%
Other values (35) 955145
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3569917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 457549
12.8%
402593
11.3%
. 344992
 
9.7%
S 283993
 
8.0%
E 264166
 
7.4%
P 219568
 
6.2%
R 187787
 
5.3%
N 179165
 
5.0%
Y 152416
 
4.3%
I 122543
 
3.4%
Other values (35) 955145
26.8%

sigla
Text

Distinct26256
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2024-11-08T21:39:53.262998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length25
Median length23
Mean length14.028709
Min length3

Characters and Unicode

Total characters3208464
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique475 ?
Unique (%)0.2%

Sample

1st rowYPF.SC.ECh-283
2nd rowYPF.SC.ECh-283
3rd rowYPF.SC.ECh-283
4th rowYPF.SC.CL-2397
5th rowYPF.Ch.EA-995
ValueCountFrequency (%)
h 99
 
< 0.1%
ene.lp.cdp 70
 
< 0.1%
pp.nq.ce-1143 36
 
< 0.1%
ypf.nq.el-7 36
 
< 0.1%
plu.nq.ce-1200-d 36
 
< 0.1%
ypf.nq.el-3 36
 
< 0.1%
ypf.sc.ech-385 36
 
< 0.1%
aps.nq.sa-1087 36
 
< 0.1%
ypf.nq.el-9 36
 
< 0.1%
ypf.sc.ech-390 36
 
< 0.1%
Other values (26271) 228917
99.8%
2024-11-08T21:39:54.073557image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 475314
 
14.8%
P 244216
 
7.6%
C 231126
 
7.2%
- 227288
 
7.1%
1 152146
 
4.7%
F 116569
 
3.6%
Y 112614
 
3.5%
S 108636
 
3.4%
N 98312
 
3.1%
2 95136
 
3.0%
Other values (61) 1347107
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3208464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 475314
 
14.8%
P 244216
 
7.6%
C 231126
 
7.2%
- 227288
 
7.1%
1 152146
 
4.7%
F 116569
 
3.6%
Y 112614
 
3.5%
S 108636
 
3.4%
N 98312
 
3.1%
2 95136
 
3.0%
Other values (61) 1347107
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3208464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 475314
 
14.8%
P 244216
 
7.6%
C 231126
 
7.2%
- 227288
 
7.1%
1 152146
 
4.7%
F 116569
 
3.6%
Y 112614
 
3.5%
S 108636
 
3.4%
N 98312
 
3.1%
2 95136
 
3.0%
Other values (61) 1347107
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3208464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 475314
 
14.8%
P 244216
 
7.6%
C 231126
 
7.2%
- 227288
 
7.1%
1 152146
 
4.7%
F 116569
 
3.6%
Y 112614
 
3.5%
S 108636
 
3.4%
N 98312
 
3.1%
2 95136
 
3.0%
Other values (61) 1347107
42.0%
Distinct66
Distinct (%)< 0.1%
Missing184
Missing (%)0.1%
Memory size1.7 MiB
2024-11-08T21:39:54.480739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters914092
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOBI
2nd rowPOD1
3rd rowCAST
4th rowPOD1
5th rowGCHU
ValueCountFrequency (%)
cori 38185
16.7%
bbar 37690
16.5%
vmut 17096
 
7.5%
csec 16855
 
7.4%
gchu 15049
 
6.6%
qtuc 10820
 
4.7%
melc 9350
 
4.1%
laja 8276
 
3.6%
cent 7751
 
3.4%
agri 6914
 
3.0%
Other values (56) 60537
26.5%
2024-11-08T21:39:55.166373image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 127084
13.9%
R 109222
11.9%
B 86959
9.5%
A 85941
9.4%
O 64055
 
7.0%
I 58394
 
6.4%
U 54809
 
6.0%
T 51238
 
5.6%
E 44011
 
4.8%
L 39640
 
4.3%
Other values (14) 192739
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 914092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 127084
13.9%
R 109222
11.9%
B 86959
9.5%
A 85941
9.4%
O 64055
 
7.0%
I 58394
 
6.4%
U 54809
 
6.0%
T 51238
 
5.6%
E 44011
 
4.8%
L 39640
 
4.3%
Other values (14) 192739
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 914092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 127084
13.9%
R 109222
11.9%
B 86959
9.5%
A 85941
9.4%
O 64055
 
7.0%
I 58394
 
6.4%
U 54809
 
6.0%
T 51238
 
5.6%
E 44011
 
4.8%
L 39640
 
4.3%
Other values (14) 192739
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 914092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 127084
13.9%
R 109222
11.9%
B 86959
9.5%
A 85941
9.4%
O 64055
 
7.0%
I 58394
 
6.4%
U 54809
 
6.0%
T 51238
 
5.6%
E 44011
 
4.8%
L 39640
 
4.3%
Other values (14) 192739
21.1%

profundidad
Real number (ℝ)

Skewed  Zeros 

Distinct4779
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2050.1756
Minimum0
Maximum378939
Zeros14875
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2024-11-08T21:39:55.520076image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11256
median1910
Q32530
95-th percentile4798
Maximum378939
Range378939
Interquartile range (IQR)1274

Descriptive statistics

Standard deviation2672.3888
Coefficient of variation (CV)1.3034926
Kurtosis15565.292
Mean2050.1756
Median Absolute Deviation (MAD)639
Skewness110.51835
Sum4.6888952 × 108
Variance7141661.7
MonotonicityNot monotonic
2024-11-08T21:39:55.858637image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14875
 
6.5%
1200 1392
 
0.6%
1600 1324
 
0.6%
2400 1259
 
0.6%
1400 1163
 
0.5%
1500 1145
 
0.5%
1300 1134
 
0.5%
2300 1086
 
0.5%
2250 1066
 
0.5%
2000 1056
 
0.5%
Other values (4769) 203207
88.9%
ValueCountFrequency (%)
0 14875
6.5%
1 36
 
< 0.1%
2 9
 
< 0.1%
2.016 9
 
< 0.1%
2.454 9
 
< 0.1%
3.21 9
 
< 0.1%
3.23 9
 
< 0.1%
3.235 9
 
< 0.1%
3.253 7
 
< 0.1%
3.275 9
 
< 0.1%
ValueCountFrequency (%)
378939 9
< 0.1%
20680 9
< 0.1%
15273 8
< 0.1%
8687 8
< 0.1%
8530 9
< 0.1%
7784 9
< 0.1%
7710 9
< 0.1%
7537 8
< 0.1%
7535 9
< 0.1%
7523 9
< 0.1%
Distinct65
Distinct (%)< 0.1%
Missing204
Missing (%)0.1%
Memory size1.7 MiB
2024-11-08T21:39:56.249019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length26
Median length19
Mean length11.677987
Min length4

Characters and Unicode

Total characters2668455
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtobífera
2nd rowpozo d-129
3rd rowcastillo
4th rowpozo d-129
5th rowgrupo chubut
ValueCountFrequency (%)
comodoro 38185
 
9.7%
rivadavia 38185
 
9.7%
bajo 37690
 
9.6%
barreal 37690
 
9.6%
grupo 18171
 
4.6%
vaca 17096
 
4.3%
muerta 17096
 
4.3%
cañadon 16855
 
4.3%
seco 16855
 
4.3%
chubut 15049
 
3.8%
Other values (86) 141749
35.9%
2024-11-08T21:39:57.352780image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 425102
15.9%
o 331820
12.4%
r 258816
 
9.7%
166224
 
6.2%
c 162029
 
6.1%
i 153481
 
5.8%
e 132171
 
5.0%
l 107701
 
4.0%
u 106217
 
4.0%
b 103829
 
3.9%
Other values (28) 721065
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2668455
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 425102
15.9%
o 331820
12.4%
r 258816
 
9.7%
166224
 
6.2%
c 162029
 
6.1%
i 153481
 
5.8%
e 132171
 
5.0%
l 107701
 
4.0%
u 106217
 
4.0%
b 103829
 
3.9%
Other values (28) 721065
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2668455
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 425102
15.9%
o 331820
12.4%
r 258816
 
9.7%
166224
 
6.2%
c 162029
 
6.1%
i 153481
 
5.8%
e 132171
 
5.0%
l 107701
 
4.0%
u 106217
 
4.0%
b 103829
 
3.9%
Other values (28) 721065
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2668455
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 425102
15.9%
o 331820
12.4%
r 258816
 
9.7%
166224
 
6.2%
c 162029
 
6.1%
i 153481
 
5.8%
e 132171
 
5.0%
l 107701
 
4.0%
u 106217
 
4.0%
b 103829
 
3.9%
Other values (28) 721065
27.0%
Distinct249
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2024-11-08T21:39:58.300984image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1726838
Min length3

Characters and Unicode

Total characters725615
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYAT
2nd rowYAT
3rd rowYAT
4th rowCLM
5th rowBEH
ValueCountFrequency (%)
ang 31039
 
13.6%
per 12323
 
5.4%
esc 11635
 
5.1%
beh 9059
 
4.0%
csn 6531
 
2.9%
cuy 5929
 
2.6%
lcam 5634
 
2.5%
clm 5435
 
2.4%
dia 5183
 
2.3%
ldl 4848
 
2.1%
Other values (239) 131091
57.3%
2024-11-08T21:39:59.877552image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 91775
12.6%
A 85866
11.8%
E 73245
10.1%
N 54230
 
7.5%
L 51647
 
7.1%
P 48458
 
6.7%
S 39205
 
5.4%
G 38412
 
5.3%
R 35619
 
4.9%
O 25234
 
3.5%
Other values (25) 181924
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 725615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 91775
12.6%
A 85866
11.8%
E 73245
10.1%
N 54230
 
7.5%
L 51647
 
7.1%
P 48458
 
6.7%
S 39205
 
5.4%
G 38412
 
5.3%
R 35619
 
4.9%
O 25234
 
3.5%
Other values (25) 181924
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 725615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 91775
12.6%
A 85866
11.8%
E 73245
10.1%
N 54230
 
7.5%
L 51647
 
7.1%
P 48458
 
6.7%
S 39205
 
5.4%
G 38412
 
5.3%
R 35619
 
4.9%
O 25234
 
3.5%
Other values (25) 181924
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 725615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 91775
12.6%
A 85866
11.8%
E 73245
10.1%
N 54230
 
7.5%
L 51647
 
7.1%
P 48458
 
6.7%
S 39205
 
5.4%
G 38412
 
5.3%
R 35619
 
4.9%
O 25234
 
3.5%
Other values (25) 181924
25.1%
Distinct249
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2024-11-08T21:40:00.512806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length41
Median length31
Mean length21.343028
Min length5

Characters and Unicode

Total characters4881300
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCAÑADON YATEL
2nd rowCAÑADON YATEL
3rd rowCAÑADON YATEL
4th rowCAÑADON LEON - MESETA ESPINOSA
5th rowMANANTIALES BEHR
ValueCountFrequency (%)
100098
 
11.6%
cerro 41083
 
4.8%
dragon 34145
 
4.0%
la 33839
 
3.9%
de 33259
 
3.8%
grande 32764
 
3.8%
anticlinal 31885
 
3.7%
el 29539
 
3.4%
las 26924
 
3.1%
cañadon 25204
 
2.9%
Other values (308) 475192
55.0%
2024-11-08T21:40:01.425505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 687274
14.1%
643827
13.2%
E 464800
9.5%
R 345470
 
7.1%
L 338353
 
6.9%
N 316887
 
6.5%
O 302976
 
6.2%
D 264701
 
5.4%
S 237617
 
4.9%
C 215034
 
4.4%
Other values (31) 1064361
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4881300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 687274
14.1%
643827
13.2%
E 464800
9.5%
R 345470
 
7.1%
L 338353
 
6.9%
N 316887
 
6.5%
O 302976
 
6.2%
D 264701
 
5.4%
S 237617
 
4.9%
C 215034
 
4.4%
Other values (31) 1064361
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4881300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 687274
14.1%
643827
13.2%
E 464800
9.5%
R 345470
 
7.1%
L 338353
 
6.9%
N 316887
 
6.5%
O 302976
 
6.2%
D 264701
 
5.4%
S 237617
 
4.9%
C 215034
 
4.4%
Other values (31) 1064361
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4881300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 687274
14.1%
643827
13.2%
E 464800
9.5%
R 345470
 
7.1%
L 338353
 
6.9%
N 316887
 
6.5%
O 302976
 
6.2%
D 264701
 
5.4%
S 237617
 
4.9%
C 215034
 
4.4%
Other values (31) 1064361
21.8%
Distinct575
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2024-11-08T21:40:02.118616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.4436506
Min length2

Characters and Unicode

Total characters787587
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowZ157
2nd rowZ157
3rd rowZ157
4th rowCLEO
5th rowBEH
ValueCountFrequency (%)
per 9680
 
4.2%
beh 9059
 
4.0%
dia 5183
 
2.3%
vahe 4783
 
2.1%
z071 4775
 
2.1%
zor 4768
 
2.1%
lcll 4604
 
2.0%
nit 4262
 
1.9%
z089 3297
 
1.4%
las2 3199
 
1.4%
Other values (565) 175097
76.6%
2024-11-08T21:40:03.212956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 77433
 
9.8%
C 72030
 
9.1%
L 66475
 
8.4%
A 60967
 
7.7%
R 54854
 
7.0%
P 38270
 
4.9%
Z 37598
 
4.8%
S 33235
 
4.2%
O 32964
 
4.2%
I 28249
 
3.6%
Other values (27) 285512
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 787587
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 77433
 
9.8%
C 72030
 
9.1%
L 66475
 
8.4%
A 60967
 
7.7%
R 54854
 
7.0%
P 38270
 
4.9%
Z 37598
 
4.8%
S 33235
 
4.2%
O 32964
 
4.2%
I 28249
 
3.6%
Other values (27) 285512
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 787587
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 77433
 
9.8%
C 72030
 
9.1%
L 66475
 
8.4%
A 60967
 
7.7%
R 54854
 
7.0%
P 38270
 
4.9%
Z 37598
 
4.8%
S 33235
 
4.2%
O 32964
 
4.2%
I 28249
 
3.6%
Other values (27) 285512
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 787587
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 77433
 
9.8%
C 72030
 
9.1%
L 66475
 
8.4%
A 60967
 
7.7%
R 54854
 
7.0%
P 38270
 
4.9%
Z 37598
 
4.8%
S 33235
 
4.2%
O 32964
 
4.2%
I 28249
 
3.6%
Other values (27) 285512
36.3%
Distinct531
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2024-11-08T21:40:03.802907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length43
Median length34
Mean length13.960508
Min length2

Characters and Unicode

Total characters3192866
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowESTANCIA CHOLITA
2nd rowESTANCIA CHOLITA
3rd rowESTANCIA CHOLITA
4th rowCAÑADON LEON
5th rowMANANTIALES BEHR
ValueCountFrequency (%)
el 28439
 
5.4%
la 18787
 
3.6%
cañadon 17448
 
3.3%
de 17219
 
3.3%
cerro 13261
 
2.5%
los 12030
 
2.3%
sur 9835
 
1.9%
loma 9696
 
1.8%
perales 9680
 
1.8%
manantiales 9059
 
1.7%
Other values (504) 378971
72.3%
2024-11-08T21:40:04.851383image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 455623
14.3%
E 330740
10.4%
297248
 
9.3%
O 245333
 
7.7%
L 240656
 
7.5%
R 215481
 
6.7%
N 171919
 
5.4%
S 168855
 
5.3%
D 157232
 
4.9%
C 147022
 
4.6%
Other values (42) 762757
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3192866
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 455623
14.3%
E 330740
10.4%
297248
 
9.3%
O 245333
 
7.7%
L 240656
 
7.5%
R 215481
 
6.7%
N 171919
 
5.4%
S 168855
 
5.3%
D 157232
 
4.9%
C 147022
 
4.6%
Other values (42) 762757
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3192866
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 455623
14.3%
E 330740
10.4%
297248
 
9.3%
O 245333
 
7.7%
L 240656
 
7.5%
R 215481
 
6.7%
N 171919
 
5.4%
S 168855
 
5.3%
D 157232
 
4.9%
C 147022
 
4.6%
Other values (42) 762757
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3192866
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 455623
14.3%
E 330740
10.4%
297248
 
9.3%
O 245333
 
7.7%
L 240656
 
7.5%
R 215481
 
6.7%
N 171919
 
5.4%
S 168855
 
5.3%
D 157232
 
4.9%
C 147022
 
4.6%
Other values (42) 762757
23.9%

cuenca
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
GOLFO SAN JORGE
122356 
NEUQUINA
92395 
CUYANA
 
7609
AUSTRAL
 
5638
NOROESTE
 
709

Length

Max length15
Median length15
Mean length11.65374
Min length6

Characters and Unicode

Total characters2665292
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGOLFO SAN JORGE
2nd rowGOLFO SAN JORGE
3rd rowGOLFO SAN JORGE
4th rowGOLFO SAN JORGE
5th rowGOLFO SAN JORGE

Common Values

ValueCountFrequency (%)
GOLFO SAN JORGE 122356
53.5%
NEUQUINA 92395
40.4%
CUYANA 7609
 
3.3%
AUSTRAL 5638
 
2.5%
NOROESTE 709
 
0.3%

Length

2024-11-08T21:40:05.245261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T21:40:05.497237image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
golfo 122356
25.8%
san 122356
25.8%
jorge 122356
25.8%
neuquina 92395
19.5%
cuyana 7609
 
1.6%
austral 5638
 
1.2%
noroeste 709
 
0.1%

Most occurring characters

ValueCountFrequency (%)
O 368486
13.8%
N 315464
11.8%
G 244712
9.2%
244712
9.2%
A 241245
9.1%
E 216169
8.1%
U 198037
7.4%
S 128703
 
4.8%
R 128703
 
4.8%
L 127994
 
4.8%
Other values (7) 451067
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2665292
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 368486
13.8%
N 315464
11.8%
G 244712
9.2%
244712
9.2%
A 241245
9.1%
E 216169
8.1%
U 198037
7.4%
S 128703
 
4.8%
R 128703
 
4.8%
L 127994
 
4.8%
Other values (7) 451067
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2665292
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 368486
13.8%
N 315464
11.8%
G 244712
9.2%
244712
9.2%
A 241245
9.1%
E 216169
8.1%
U 198037
7.4%
S 128703
 
4.8%
R 128703
 
4.8%
L 127994
 
4.8%
Other values (7) 451067
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2665292
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 368486
13.8%
N 315464
11.8%
G 244712
9.2%
244712
9.2%
A 241245
9.1%
E 216169
8.1%
U 198037
7.4%
S 128703
 
4.8%
R 128703
 
4.8%
L 127994
 
4.8%
Other values (7) 451067
16.9%

provincia
Categorical

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Santa Cruz
65860 
Chubut
59959 
Neuquén
53587 
Mendoza
22500 
Rio Negro
13880 
Other values (6)
12921 

Length

Max length16
Median length15
Mean length7.846769
Min length5

Characters and Unicode

Total characters1794611
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSanta Cruz
2nd rowSanta Cruz
3rd rowSanta Cruz
4th rowSanta Cruz
5th rowChubut

Common Values

ValueCountFrequency (%)
Santa Cruz 65860
28.8%
Chubut 59959
26.2%
Neuquén 53587
23.4%
Mendoza 22500
 
9.8%
Rio Negro 13880
 
6.1%
La Pampa 10037
 
4.4%
Tierra del Fuego 2038
 
0.9%
Salta 495
 
0.2%
Estado Nacional 137
 
0.1%
Formosa 112
 
< 0.1%

Length

2024-11-08T21:40:05.804445image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
santa 65860
20.4%
cruz 65860
20.4%
chubut 59959
18.6%
neuquén 53587
16.6%
mendoza 22500
 
7.0%
rio 13880
 
4.3%
negro 13880
 
4.3%
pampa 10037
 
3.1%
la 10037
 
3.1%
tierra 2038
 
0.6%
Other values (7) 5059
 
1.6%

Most occurring characters

ValueCountFrequency (%)
u 295194
16.4%
a 187882
 
10.5%
n 142084
 
7.9%
t 126451
 
7.0%
C 125819
 
7.0%
e 96081
 
5.4%
93990
 
5.2%
z 88360
 
4.9%
r 83928
 
4.7%
N 67604
 
3.8%
Other values (24) 487218
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1794611
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 295194
16.4%
a 187882
 
10.5%
n 142084
 
7.9%
t 126451
 
7.0%
C 125819
 
7.0%
e 96081
 
5.4%
93990
 
5.2%
z 88360
 
4.9%
r 83928
 
4.7%
N 67604
 
3.8%
Other values (24) 487218
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1794611
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 295194
16.4%
a 187882
 
10.5%
n 142084
 
7.9%
t 126451
 
7.0%
C 125819
 
7.0%
e 96081
 
5.4%
93990
 
5.2%
z 88360
 
4.9%
r 83928
 
4.7%
N 67604
 
3.8%
Other values (24) 487218
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1794611
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 295194
16.4%
a 187882
 
10.5%
n 142084
 
7.9%
t 126451
 
7.0%
C 125819
 
7.0%
e 96081
 
5.4%
93990
 
5.2%
z 88360
 
4.9%
r 83928
 
4.7%
N 67604
 
3.8%
Other values (24) 487218
27.1%

tipo_de_recurso
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
CONVENCIONAL
200613 
NO CONVENCIONAL
28083 
NO DISCRIMINADO
 
9
SIN RESERVORIO
 
2

Length

Max length15
Median length12
Mean length12.368506
Min length12

Characters and Unicode

Total characters2828764
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCONVENCIONAL
2nd rowCONVENCIONAL
3rd rowCONVENCIONAL
4th rowCONVENCIONAL
5th rowCONVENCIONAL

Common Values

ValueCountFrequency (%)
CONVENCIONAL 200613
87.7%
NO CONVENCIONAL 28083
 
12.3%
NO DISCRIMINADO 9
 
< 0.1%
SIN RESERVORIO 2
 
< 0.1%

Length

2024-11-08T21:40:06.110919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T21:40:06.353430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
convencional 228696
89.1%
no 28092
 
10.9%
discriminado 9
 
< 0.1%
sin 2
 
< 0.1%
reservorio 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 714191
25.2%
O 485497
17.2%
C 457401
16.2%
I 228727
 
8.1%
A 228705
 
8.1%
E 228700
 
8.1%
V 228698
 
8.1%
L 228696
 
8.1%
28094
 
1.0%
D 18
 
< 0.1%
Other values (3) 37
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2828764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 714191
25.2%
O 485497
17.2%
C 457401
16.2%
I 228727
 
8.1%
A 228705
 
8.1%
E 228700
 
8.1%
V 228698
 
8.1%
L 228696
 
8.1%
28094
 
1.0%
D 18
 
< 0.1%
Other values (3) 37
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2828764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 714191
25.2%
O 485497
17.2%
C 457401
16.2%
I 228727
 
8.1%
A 228705
 
8.1%
E 228700
 
8.1%
V 228698
 
8.1%
L 228696
 
8.1%
28094
 
1.0%
D 18
 
< 0.1%
Other values (3) 37
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2828764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 714191
25.2%
O 485497
17.2%
C 457401
16.2%
I 228727
 
8.1%
A 228705
 
8.1%
E 228700
 
8.1%
V 228698
 
8.1%
L 228696
 
8.1%
28094
 
1.0%
D 18
 
< 0.1%
Other values (3) 37
 
< 0.1%

proyecto
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Sin Proyecto
221422 
GAS PLUS
 
7285

Length

Max length12
Median length12
Mean length11.872588
Min length8

Characters and Unicode

Total characters2715344
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSin Proyecto
2nd rowSin Proyecto
3rd rowSin Proyecto
4th rowSin Proyecto
5th rowSin Proyecto

Common Values

ValueCountFrequency (%)
Sin Proyecto 221422
96.8%
GAS PLUS 7285
 
3.2%

Length

2024-11-08T21:40:06.665018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T21:40:06.895348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
sin 221422
48.4%
proyecto 221422
48.4%
gas 7285
 
1.6%
plus 7285
 
1.6%

Most occurring characters

ValueCountFrequency (%)
o 442844
16.3%
S 235992
8.7%
228707
8.4%
P 228707
8.4%
i 221422
8.2%
n 221422
8.2%
r 221422
8.2%
y 221422
8.2%
e 221422
8.2%
c 221422
8.2%
Other values (5) 250562
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2715344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 442844
16.3%
S 235992
8.7%
228707
8.4%
P 228707
8.4%
i 221422
8.2%
n 221422
8.2%
r 221422
8.2%
y 221422
8.2%
e 221422
8.2%
c 221422
8.2%
Other values (5) 250562
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2715344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 442844
16.3%
S 235992
8.7%
228707
8.4%
P 228707
8.4%
i 221422
8.2%
n 221422
8.2%
r 221422
8.2%
y 221422
8.2%
e 221422
8.2%
c 221422
8.2%
Other values (5) 250562
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2715344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 442844
16.3%
S 235992
8.7%
228707
8.4%
P 228707
8.4%
i 221422
8.2%
n 221422
8.2%
r 221422
8.2%
y 221422
8.2%
e 221422
8.2%
c 221422
8.2%
Other values (5) 250562
9.2%

clasificacion
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing33651
Missing (%)14.7%
Memory size1.7 MiB
EXPLOTACION
187553 
EXPLORACION
 
6504
SERVICIO
 
963
ALMACENAMIENTO
 
36

Length

Max length14
Median length11
Mean length10.985743
Min length8

Characters and Unicode

Total characters2142835
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEXPLOTACION
2nd rowEXPLOTACION
3rd rowEXPLOTACION
4th rowEXPLOTACION
5th rowEXPLOTACION

Common Values

ValueCountFrequency (%)
EXPLOTACION 187553
82.0%
EXPLORACION 6504
 
2.8%
SERVICIO 963
 
0.4%
ALMACENAMIENTO 36
 
< 0.1%
(Missing) 33651
 
14.7%

Length

2024-11-08T21:40:07.142008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T21:40:07.391557image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
explotacion 187553
96.2%
exploracion 6504
 
3.3%
servicio 963
 
0.5%
almacenamiento 36
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 389113
18.2%
I 196019
9.1%
E 195092
9.1%
C 195056
9.1%
A 194165
9.1%
N 194129
9.1%
L 194093
9.1%
X 194057
9.1%
P 194057
9.1%
T 187589
8.8%
Other values (4) 9465
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2142835
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 389113
18.2%
I 196019
9.1%
E 195092
9.1%
C 195056
9.1%
A 194165
9.1%
N 194129
9.1%
L 194093
9.1%
X 194057
9.1%
P 194057
9.1%
T 187589
8.8%
Other values (4) 9465
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2142835
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 389113
18.2%
I 196019
9.1%
E 195092
9.1%
C 195056
9.1%
A 194165
9.1%
N 194129
9.1%
L 194093
9.1%
X 194057
9.1%
P 194057
9.1%
T 187589
8.8%
Other values (4) 9465
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2142835
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 389113
18.2%
I 196019
9.1%
E 195092
9.1%
C 195056
9.1%
A 194165
9.1%
N 194129
9.1%
L 194093
9.1%
X 194057
9.1%
P 194057
9.1%
T 187589
8.8%
Other values (4) 9465
 
0.4%

subclasificacion
Categorical

High correlation  Imbalance  Missing 

Distinct12
Distinct (%)< 0.1%
Missing33651
Missing (%)14.7%
Memory size1.7 MiB
DESARROLLO
173678 
AVANZADA
 
14438
EXPLORACION
 
3927
EXTENSION
 
1156
INYECTOR DE AGUA
 
947
Other values (7)
 
910

Length

Max length21
Median length10
Mean length9.9406068
Min length7

Characters and Unicode

Total characters1938975
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDESARROLLO
2nd rowDESARROLLO
3rd rowDESARROLLO
4th rowDESARROLLO
5th rowDESARROLLO

Common Values

ValueCountFrequency (%)
DESARROLLO 173678
75.9%
AVANZADA 14438
 
6.3%
EXPLORACION 3927
 
1.7%
EXTENSION 1156
 
0.5%
INYECTOR DE AGUA 947
 
0.4%
EXPLORATORIO PROFUNDO 748
 
0.3%
EXPLORATORIO SOMERO 70
 
< 0.1%
CONTROL 36
 
< 0.1%
ESTUDIO 27
 
< 0.1%
INYECTOR DE GAS 18
 
< 0.1%
Other values (2) 11
 
< 0.1%
(Missing) 33651
 
14.7%

Length

2024-11-08T21:40:07.709343image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
desarrollo 173678
87.8%
avanzada 14438
 
7.3%
exploracion 3927
 
2.0%
extension 1156
 
0.6%
inyector 976
 
0.5%
de 974
 
0.5%
agua 947
 
0.5%
exploratorio 818
 
0.4%
profundo 748
 
0.4%
somero 70
 
< 0.1%
Other values (5) 92
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 361540
18.6%
R 354762
18.3%
L 352137
18.2%
A 238100
12.3%
D 189865
9.8%
E 182784
9.4%
S 174949
9.0%
N 22437
 
1.2%
V 14447
 
0.7%
Z 14438
 
0.7%
Other values (11) 33516
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1938975
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 361540
18.6%
R 354762
18.3%
L 352137
18.2%
A 238100
12.3%
D 189865
9.8%
E 182784
9.4%
S 174949
9.0%
N 22437
 
1.2%
V 14447
 
0.7%
Z 14438
 
0.7%
Other values (11) 33516
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1938975
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 361540
18.6%
R 354762
18.3%
L 352137
18.2%
A 238100
12.3%
D 189865
9.8%
E 182784
9.4%
S 174949
9.0%
N 22437
 
1.2%
V 14447
 
0.7%
Z 14438
 
0.7%
Other values (11) 33516
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1938975
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 361540
18.6%
R 354762
18.3%
L 352137
18.2%
A 238100
12.3%
D 189865
9.8%
E 182784
9.4%
S 174949
9.0%
N 22437
 
1.2%
V 14447
 
0.7%
Z 14438
 
0.7%
Other values (11) 33516
 
1.7%

sub_tipo_recurso
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing200650
Missing (%)87.7%
Memory size1.7 MiB
SHALE
16914 
TIGHT
11143 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters140285
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSHALE
2nd rowSHALE
3rd rowSHALE
4th rowSHALE
5th rowSHALE

Common Values

ValueCountFrequency (%)
SHALE 16914
 
7.4%
TIGHT 11143
 
4.9%
(Missing) 200650
87.7%

Length

2024-11-08T21:40:07.983400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T21:40:08.215574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
shale 16914
60.3%
tight 11143
39.7%

Most occurring characters

ValueCountFrequency (%)
H 28057
20.0%
T 22286
15.9%
S 16914
12.1%
A 16914
12.1%
L 16914
12.1%
E 16914
12.1%
I 11143
 
7.9%
G 11143
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140285
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
H 28057
20.0%
T 22286
15.9%
S 16914
12.1%
A 16914
12.1%
L 16914
12.1%
E 16914
12.1%
I 11143
 
7.9%
G 11143
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140285
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
H 28057
20.0%
T 22286
15.9%
S 16914
12.1%
A 16914
12.1%
L 16914
12.1%
E 16914
12.1%
I 11143
 
7.9%
G 11143
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140285
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
H 28057
20.0%
T 22286
15.9%
S 16914
12.1%
A 16914
12.1%
L 16914
12.1%
E 16914
12.1%
I 11143
 
7.9%
G 11143
 
7.9%

fecha_data
Categorical

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2024-05-31
25618 
2024-01-31
25611 
2024-02-29
25559 
2024-04-30
25554 
2024-03-31
25543 
Other values (4)
100822 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2287070
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-31
2nd row2024-01-31
3rd row2024-01-31
4th row2024-01-31
5th row2024-01-31

Common Values

ValueCountFrequency (%)
2024-05-31 25618
11.2%
2024-01-31 25611
11.2%
2024-02-29 25559
11.2%
2024-04-30 25554
11.2%
2024-03-31 25543
11.2%
2024-06-30 25346
11.1%
2024-09-30 25341
11.1%
2024-08-31 25285
11.1%
2024-07-31 24850
10.9%

Length

2024-11-08T21:40:08.444815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-08T21:40:08.742596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2024-05-31 25618
11.2%
2024-01-31 25611
11.2%
2024-02-29 25559
11.2%
2024-04-30 25554
11.2%
2024-03-31 25543
11.2%
2024-06-30 25346
11.1%
2024-09-30 25341
11.1%
2024-08-31 25285
11.1%
2024-07-31 24850
10.9%

Most occurring characters

ValueCountFrequency (%)
0 533655
23.3%
2 508532
22.2%
- 457414
20.0%
4 254261
11.1%
3 228691
10.0%
1 152518
 
6.7%
9 50900
 
2.2%
5 25618
 
1.1%
6 25346
 
1.1%
8 25285
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2287070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 533655
23.3%
2 508532
22.2%
- 457414
20.0%
4 254261
11.1%
3 228691
10.0%
1 152518
 
6.7%
9 50900
 
2.2%
5 25618
 
1.1%
6 25346
 
1.1%
8 25285
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2287070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 533655
23.3%
2 508532
22.2%
- 457414
20.0%
4 254261
11.1%
3 228691
10.0%
1 152518
 
6.7%
9 50900
 
2.2%
5 25618
 
1.1%
6 25346
 
1.1%
8 25285
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2287070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 533655
23.3%
2 508532
22.2%
- 457414
20.0%
4 254261
11.1%
3 228691
10.0%
1 152518
 
6.7%
9 50900
 
2.2%
5 25618
 
1.1%
6 25346
 
1.1%
8 25285
 
1.1%

Interactions

2024-11-08T21:39:27.485933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:52.909042image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:56.150057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:59.955646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:03.848667image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:07.137977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:10.267708image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:13.456044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:18.027640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:21.140558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:24.223932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:27.886707image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:53.252247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:56.466161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:00.370340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:04.119873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:07.411275image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:10.559369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:13.856775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:18.300366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:21.409786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:24.513464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:28.215260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:53.529304image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:56.754184image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:00.724022image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:04.374599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:07.696299image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:10.867573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:14.253654image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:18.565062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:21.701344image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:24.842474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:28.543397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:53.821701image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:57.052897image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:01.162085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:04.655777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:08.007817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:11.192920image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:14.652735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:18.848066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:21.964916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:25.141354image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:28.897227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:54.076187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:57.322413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:01.513287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:04.954782image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:08.269593image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:11.454990image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:15.006073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:19.124336image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:22.244316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:25.410430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:29.866916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:54.361405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:57.621785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:01.940469image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:05.238812image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:08.549431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:11.751369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:15.396682image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:19.404370image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:22.527806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:25.702563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:30.273231image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:54.675322image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:58.225002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:02.278641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:05.523743image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:08.835845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:12.013822image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:15.747847image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:19.716296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:22.853548image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:25.994061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:30.673220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:54.980296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:58.530711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:02.721498image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:05.802039image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:09.125334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:12.276609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:16.213979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:20.013558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:23.130316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:26.288092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:31.093231image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:55.264727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:58.911297image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:03.036549image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:06.062100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:09.396053image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:12.528267image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:16.646599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:20.282698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:23.379318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:26.571205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:31.522961image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:55.560004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:59.205799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:03.290660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:06.324954image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:09.678439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:12.793857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:17.072129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:20.572315image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:23.662103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:26.876548image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:31.985549image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:55.856061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:38:59.521297image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:03.558633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:06.573986image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:09.986748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:13.109149image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:17.768522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:20.855926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:23.928746image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-08T21:39:27.166755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-08T21:40:09.038471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
clasificacioncuencaempresafecha_dataidempresaidpozoidusuarioiny_aguainy_gasiny_otromesprod_aguaprod_gasprod_petprofundidadprovinciaproyectosub_tipo_recursosubclasificacionteftipo_de_recursotipoestadotipoextracciontipopozo
clasificacion1.0000.0810.1300.0000.1300.0900.0840.0000.0000.0060.0000.0220.0000.0070.0000.0850.0310.0550.7880.0100.0200.0310.0630.055
cuenca0.0811.0000.6410.0000.6410.2490.3150.0000.0060.0030.0000.1200.0630.0680.0060.8180.1280.3060.1000.0440.2560.0410.3400.208
empresa0.1300.6411.0000.0031.0000.4091.0000.0000.1590.3590.0030.0890.0690.1070.0000.6690.1710.5360.1390.1000.2500.1290.3240.268
fecha_data0.0000.0000.0031.0000.0030.0020.0000.0020.0070.0081.0000.0050.0010.0010.0000.0000.0030.0260.0000.0740.0080.0180.0050.000
idempresa0.1300.6411.0000.0031.0000.4091.0000.0000.1590.3590.0030.0890.0690.1070.0000.6690.1710.5360.1390.1000.2500.1290.3240.268
idpozo0.0900.2490.4090.0020.4091.000-0.077-0.0030.021-0.0100.018-0.1050.2740.1850.2540.2350.1630.2320.084-0.0440.2680.0360.1600.108
idusuario0.0840.3151.0000.0001.000-0.0771.0000.0050.0120.0170.0020.1350.0100.0950.2120.2780.1030.4830.0650.0810.1180.0640.2190.150
iny_agua0.0000.0000.0000.0020.000-0.0030.0051.000-0.000-0.0000.001-0.0220.017-0.0240.0130.0000.0001.0000.000-0.0030.0000.2270.0580.387
iny_gas0.0000.0060.1590.0070.1590.0210.012-0.0001.000-0.0000.0070.0020.0250.0260.0250.0050.0000.0270.0000.0040.0200.0000.0190.000
iny_otro0.0060.0030.3590.0080.359-0.0100.017-0.000-0.0001.000-0.006-0.0150.004-0.015-0.0110.0040.0001.0000.009-0.0140.0000.0000.0070.007
mes0.0000.0000.0031.0000.0030.0180.0020.0010.007-0.0061.000-0.006-0.006-0.0090.0100.0000.0030.0260.000-0.0110.0080.0180.0050.000
prod_agua0.0220.1200.0890.0050.089-0.1050.135-0.0220.002-0.015-0.0061.000-0.2850.498-0.1050.0900.0560.1040.0230.0870.0600.0000.2180.067
prod_gas0.0000.0630.0690.0010.0690.2740.0100.0170.0250.004-0.006-0.2851.0000.0040.3680.2170.0040.1220.013-0.0200.0750.0000.0780.072
prod_pet0.0070.0680.1070.0010.1070.1850.095-0.0240.026-0.015-0.0090.4980.0041.0000.1100.0690.0200.2770.0100.1450.1740.0050.0850.016
profundidad0.0000.0060.0000.0000.0000.2540.2120.0130.025-0.0110.010-0.1050.3680.1101.0000.0240.0000.0190.000-0.0440.0160.0000.0130.014
provincia0.0850.8180.6690.0000.6690.2350.2780.0000.0050.0040.0000.0900.2170.0690.0241.0000.1870.4490.0800.0530.3400.0350.2710.239
proyecto0.0310.1280.1710.0030.1710.1630.1030.0000.0000.0000.0030.0560.0040.0200.0000.1871.0000.5520.0460.0250.2880.0050.2930.317
sub_tipo_recurso0.0550.3060.5360.0260.5360.2320.4831.0000.0271.0000.0260.1040.1220.2770.0190.4490.5521.0000.1480.0551.0000.0710.2500.728
subclasificacion0.7880.1000.1390.0000.1390.0840.0650.0000.0000.0090.0000.0230.0130.0100.0000.0800.0460.1481.0000.0100.0480.0180.0420.064
tef0.0100.0440.1000.0740.100-0.0440.081-0.0030.004-0.014-0.0110.087-0.0200.145-0.0440.0530.0250.0550.0101.0000.0480.0700.0550.062
tipo_de_recurso0.0200.2560.2500.0080.2500.2680.1180.0000.0200.0000.0080.0600.0750.1740.0160.3400.2881.0000.0480.0481.0000.0110.4380.242
tipoestado0.0310.0410.1290.0180.1290.0360.0640.2270.0000.0000.0180.0000.0000.0050.0000.0350.0050.0710.0180.0700.0111.0000.0560.417
tipoextraccion0.0630.3400.3240.0050.3240.1600.2190.0580.0190.0070.0050.2180.0780.0850.0130.2710.2930.2500.0420.0550.4380.0561.0000.402
tipopozo0.0550.2080.2680.0000.2680.1080.1500.3870.0000.0070.0000.0670.0720.0160.0140.2390.3170.7280.0640.0620.2420.4170.4021.000

Missing values

2024-11-08T21:39:32.716702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-08T21:39:34.806888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-08T21:39:37.299236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idempresaaniomesidpozoprod_petprod_gasprod_aguainy_aguainy_gasiny_co2iny_otrotefvida_utiltipoextracciontipoestadotipopozoobservacionesfechaingresorectificadohabilitadoidusuarioempresasiglaformprodprofundidadformacionidareapermisoconcesionareapermisoconcesionidareayacimientoareayacimientocuencaprovinciatipo_de_recursoproyectoclasificacionsubclasificacionsub_tipo_recursofecha_data
0YPF202411622233.081.232.460.00.00.00.030.65NaNBombeo MecánicoExtracción EfectivaPetrolíferoNaN2024-02-16 17:45:24.72974ft334YPF S.A.YPF.SC.ECh-283TOBI2115.0tobíferaYATCAÑADON YATELZ157ESTANCIA CHOLITAGOLFO SAN JORGESanta CruzCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-01-31
1YPF20241162222169.3267.87135.190.00.00.00.030.65NaNBombeo MecánicoExtracción EfectivaPetrolíferoNaN2024-02-16 17:45:24.72974ft334YPF S.A.YPF.SC.ECh-283POD12115.0pozo d-129YATCAÑADON YATELZ157ESTANCIA CHOLITAGOLFO SAN JORGESanta CruzCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-01-31
2YPF20241162221135.4654.30108.160.00.00.00.030.65NaNBombeo MecánicoExtracción EfectivaPetrolíferoNaN2024-02-16 17:45:24.72974ft334YPF S.A.YPF.SC.ECh-283CAST2115.0castilloYATCAÑADON YATELZ157ESTANCIA CHOLITAGOLFO SAN JORGESanta CruzCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-01-31
3YPF2024116222064.401.72132.790.00.00.00.030.88NaNBombeo MecánicoExtracción EfectivaPetrolíferoNaN2024-02-16 17:45:24.72974ft334YPF S.A.YPF.SC.CL-2397POD12477.0pozo d-129CLMCAÑADON LEON - MESETA ESPINOSACLEOCAÑADON LEONGOLFO SAN JORGESanta CruzCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-01-31
4YPF20241162188257.575.77629.950.00.00.00.030.91NaNCavidad ProgresivaExtracción EfectivaPetrolíferoNaN2024-02-16 17:45:24.72974ft334YPF S.A.YPF.Ch.EA-995GCHU1745.0grupo chubutBEHMANANTIALES BEHRBEHMANANTIALES BEHRGOLFO SAN JORGEChubutCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-01-31
5YPF2024116214110.030.001186.950.00.00.00.024.26NaNBombeo MecánicoExtracción EfectivaPetrolíferoNaN2024-02-16 17:45:24.72974ft334YPF S.A.YPF.SC.CnE-1418CAST0.0castilloESCCAÑADON DE LA ESCONDIDA - LAS HERASZ071CAÑADON DE LA ESCONDIDAGOLFO SAN JORGESanta CruzCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-01-31
6YPF202411621406.690.00791.300.00.00.00.024.26NaNBombeo MecánicoExtracción EfectivaPetrolíferoNaN2024-02-16 17:45:24.72974ft334YPF S.A.YPF.SC.CnE-1418BBAR0.0bajo barrealESCCAÑADON DE LA ESCONDIDA - LAS HERASZ071CAÑADON DE LA ESCONDIDAGOLFO SAN JORGESanta CruzCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-01-31
7YPF2024116213841.7116.09484.460.00.00.00.029.90NaNBombeo MecánicoExtracción EfectivaPetrolíferoNaN2024-02-16 17:45:24.72974ft334YPF S.A.YPF.SC.CS-2286(d)CSEC2116.0cañadon secoCLMCAÑADON LEON - MESETA ESPINOSACSECCAÑADON SECOGOLFO SAN JORGESanta CruzCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-01-31
8YPF20241162137149.7412.99737.380.00.00.00.030.94NaNBombeo MecánicoExtracción EfectivaPetrolíferoNaN2024-02-16 17:45:24.72974ft334YPF S.A.YPF.SC.CS-2273CSEC2179.0cañadon secoCLMCAÑADON LEON - MESETA ESPINOSACSECCAÑADON SECOGOLFO SAN JORGESanta CruzCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-01-31
9YPF20241162135512.0679.4644.450.00.00.00.030.02NaNBombeo MecánicoExtracción EfectivaPetrolíferoNaN2024-02-16 17:45:24.72974ft334YPF S.A.YPF.Ch.M.a-763GCHU2130.0grupo chubutBEHMANANTIALES BEHRBEHMANANTIALES BEHRGOLFO SAN JORGEChubutCONVENCIONALSin ProyectoEXPLOTACIONAVANZADANaN2024-01-31
idempresaaniomesidpozoprod_petprod_gasprod_aguainy_aguainy_gasiny_co2iny_otrotefvida_utiltipoextracciontipoestadotipopozoobservacionesfechaingresorectificadohabilitadoidusuarioempresasiglaformprodprofundidadformacionidareapermisoconcesionareapermisoconcesionidareayacimientoareayacimientocuencaprovinciatipo_de_recursoproyectoclasificacionsubclasificacionsub_tipo_recursofecha_data
228697ACO20249811352.47326919.053963405.3807780.00.00.00.030.000000NaNBombeo MecánicoExtracción EfectivaPetrolíferoACO2024-10-10 19:23:14.255711ft459Petrolera Aconcagua Energia S.A.PPC.RN.PB-130TORD2476.0tordilloELOENTRE LOMASPBLPIEDRAS BLANCASNEUQUINARio NegroCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-09-30
228698ACO20249816130.3540636.425669813.4416060.00.00.00.029.995833NaNBombeo MecánicoParado TransitoriamentePetrolíferoACO2024-10-10 19:23:14.255711ft459Petrolera Aconcagua Energia S.A.PEL.RN.PB-136PROS2387.0punta rosadaELOENTRE LOMASPBLPIEDRAS BLANCASNEUQUINARio NegroCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-09-30
228699ACO2024913460031.36104326.378602732.9580160.00.00.00.029.795833NaNBombeo MecánicoParado TransitoriamentePetrolíferoACO2024-10-10 19:23:14.255711ft459Petrolera Aconcagua Energia S.A.PEL.RN.PB-200TORD2425.0tordilloELOENTRE LOMASPBLPIEDRAS BLANCASNEUQUINARio NegroCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-09-30
228700ACO202498171137.95233913.438773750.2316830.00.00.00.029.120833NaNBombeo MecánicoExtracción EfectivaPetrolíferoACO2024-10-10 19:23:14.255711ft459Petrolera Aconcagua Energia S.A.PEL.RN.PB-146PROS2340.0punta rosadaELOENTRE LOMASPBLPIEDRAS BLANCASNEUQUINARio NegroCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-09-30
228701ACO20249817914.849730149.42091536.0096880.00.00.00.026.579167NaNSurgencia NaturalExtracción EfectivaGasíferoACO2024-10-10 19:23:14.255711ft459Petrolera Aconcagua Energia S.A.PEL.RN.PB.xp-154TORD2910.0tordilloELOENTRE LOMASPBLPIEDRAS BLANCASNEUQUINARio NegroCONVENCIONALSin ProyectoEXPLORACIONEXPLORATORIO PROFUNDONaN2024-09-30
228702ACO202491196948.4282104.405380188.9663740.00.00.00.024.804167NaNBombeo MecánicoExtracción EfectivaPetrolíferoACO2024-10-10 19:23:14.255711ft459Petrolera Aconcagua Energia S.A.PEL.RN.PB-161TORD2343.0tordilloELOENTRE LOMASPBLPIEDRAS BLANCASNEUQUINARio NegroCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-09-30
228703ACO2024913483026.7335147.585951728.0577620.00.00.00.030.000000NaNBombeo MecánicoExtracción EfectivaPetrolíferoACO2024-10-10 19:23:14.255711ft459Petrolera Aconcagua Energia S.A.PEL.RN.PB-199QTUC2450.0quintucoELOENTRE LOMASPBLPIEDRAS BLANCASNEUQUINARio NegroCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-09-30
228704ACO2024912732815.09422312.96646097.6476530.00.00.00.030.000000NaNBombeo MecánicoExtracción EfectivaPetrolíferoACO2024-10-10 19:23:14.255711ft459Petrolera Aconcagua Energia S.A.PEL.RN.PB-176QTUC2463.0quintucoELOENTRE LOMASPBLPIEDRAS BLANCASNEUQUINARio NegroCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-09-30
228705ACO202491295725.4496202.988729170.5235780.00.00.00.06.541667NaNBombeo MecánicoExtracción EfectivaPetrolíferoACO2024-10-10 19:23:14.255711ft459Petrolera Aconcagua Energia S.A.PEL.RN.PB-183TORD2345.0tordilloELOENTRE LOMASPBLPIEDRAS BLANCASNEUQUINARio NegroCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-09-30
228706ACO2024913356141.02988713.3344861224.1602230.00.00.00.016.479167NaNBombeo MecánicoExtracción EfectivaPetrolíferoACO2024-10-10 19:23:14.255711ft459Petrolera Aconcagua Energia S.A.PEL.RN.PB-197TORD2450.0tordilloELOENTRE LOMASPBLPIEDRAS BLANCASNEUQUINARio NegroCONVENCIONALSin ProyectoEXPLOTACIONDESARROLLONaN2024-09-30